Neuroscience-inspired, neuromimetic machine vision and pattern recognition algorithms are of interest for applications related to climate change monitoring, change detection, and Land Use/Land Cover (LULC) classification using satellite image data. However, these approaches frequently are not robust for multiple classes that are spatially mixed. Furthermore, despite the vast archives of globally distributed remotely sensed data collected over the last four decades and the availability of computing resources to process these datasets, global assessment of all but the simplest landscape features is not currently possible. This limitation arises from the substantial human intervention needed to accurately train current state-of-the-art feature extraction software and the limited applicability of resulting feature extraction solutions to multiple images, both spatially and temporally. Without more robust and adaptive methods to identify and extract land surface features, Earth scientists lack critical datasets needed to parameterize models and quantify climatic and land use driven changes.
Land cover classification in satellite imagery presents a signal processing challenge, usually due to the lack of verified pixel-level ground truth information. A large number of proprietary and open source applications exist to process remote sensing data and generate land cover classification using, for example, normalized difference vegetative index (NDVI) information or unsupervised clustering of raw pixels. These techniques rely heavily on domain expertise and usually require human input.
Other land cover classification approaches involve the use of state-of-the-art genetic algorithms, such as GENIE™, to extract a particular class of interest in a supervised manner. GENIE™ and GeniePro™ are feature extraction tools developed at Los Alamos National Laboratory for multispectral, hyperspectral, panchromatic, and multi-instrument fused imagery, but they also require supervision in training. One of the main limitations is the difficulty in providing clean training data, i.e., only pixels that truly belong to the class of interest. This is easier to do when the classes are well separated spatially from other classes, such as separating bodies of water from land or golf courses from buildings. However, this is much more difficult to do when the classes are spatially mixed (e.g., tundra shrubs and grasses) at a pixel or subpixel level.
Conventional techniques have been successful for lower-resolution satellite data, such as Landsat imagery, but they rely heavily on domain expertise and frequently require human input or supervision. These conventional techniques perform well on certain types of problems, such as distinguishing between water and land features (e.g., automatic lake detection), or specific vegetative analysis, but they are usually not robust for spatially mixed classes. The quality of spectral unmixing plays an important role in extracting information and features from low-resolution pixels, and historically, it has been a typical step in processing and classifying multispectral and hyperspectral data.
In the case of WorldView-2™ and WorldView-3™ multispectral imagery, however, spectral unmixing is less of a problem due to the much enhanced pixel resolution. The challenge instead lies in context-based information extraction using spatial and spectral texture generated by adjacent pixels. The textural patterns in a given image also need to be learned based on the wider-area context provided by the full image scene, usually necessitating significant computational resources and creative software optimization. Many current users of multispectral WorldView-2™ data resort to downsampling the image to equivalent Landsat resolution in order to facilitate feature extraction using available commercial applications.
A fundamental problem to creating scalable feature extraction technology capable of processing imagery datasets at global scales is the overconstrained training needed to generate effective solutions. Many features of environmental importance including, but not limited to, rivers, water bodies, coastlines, glaciers, and vegetation boundaries, are readily recognizable to humans based on a simple set of attributes. The very best of current feature extraction software, e.g., LANL-developed GeniePro™ however, requires extensive, image-specific training that leads to a solution with limited applicability to images other than the image used for training. Accordingly, developing automatic, unsupervised feature extraction and high-resolution, pixel-level classification tools may be beneficial and have a significant impact for a number of application areas, e.g., for studying climate change effects and providing the climate change community with more exact ways of detecting yearly and seasonal changes.